{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The text.latex.preview rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The mathtext.fallback_to_cm rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: Support for setting the 'mathtext.fallback_to_cm' rcParam is deprecated since 3.3 and will be removed two minor releases later; use 'mathtext.fallback : 'cm' instead.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The validate_bool_maybe_none function was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The savefig.jpeg_quality rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The keymap.all_axes rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_path rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n",
      "In C:\\Users\\Han\\.conda\\envs\\my_project1\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test.mplstyle: \n",
      "The animation.avconv_args rcparam was deprecated in Matplotlib 3.3 and will be removed two minor releases later.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc(\"font\", family = 'Malgun Gothic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(r\".xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_content = df['experience']\n",
    "vector = TfidfVectorizer(max_df=0.4,         # drop words that occur in more than X percent of documents\n",
    "                             min_df=1,      # only use words that appear at least X times\n",
    "                             lowercase=True, # Convert everything to lower case \n",
    "                             use_idf=True,   # Use idf\n",
    "                             norm=u'l2',     # Normalization\n",
    "                             smooth_idf=True # Prevents divide-by-zero errors\n",
    "                            )\n",
    "features = vector.fit_transform(text_content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 여러개의 클러스터링 갯수를 List로 입력 받아 각각의 실루엣 계수를 면적으로 시각화한 함수 작성\n",
    "def visualize_silhouette(cluster_lists, X_features): \n",
    "    \n",
    "    from sklearn.datasets import make_blobs\n",
    "    from sklearn.cluster import KMeans\n",
    "    from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "    import matplotlib.cm as cm\n",
    "    import math\n",
    "    \n",
    "    # 입력값으로 클러스터링 갯수들을 리스트로 받아서, 각 갯수별로 클러스터링을 적용하고 실루엣 개수를 구함\n",
    "    n_cols = len(cluster_lists)\n",
    "    \n",
    "    # plt.subplots()으로 리스트에 기재된 클러스터링 수만큼의 sub figures를 가지는 axs 생성 \n",
    "    fig, axs = plt.subplots(figsize=(4*n_cols, 4), nrows=1, ncols=n_cols)\n",
    "    \n",
    "    # 리스트에 기재된 클러스터링 갯수들을 차례로 iteration 수행하면서 실루엣 개수 시각화\n",
    "    for ind, n_cluster in enumerate(cluster_lists):\n",
    "        \n",
    "        # KMeans 클러스터링 수행하고, 실루엣 스코어와 개별 데이터의 실루엣 값 계산. \n",
    "        clusterer = KMeans(n_clusters = n_cluster, max_iter=500, random_state=0)\n",
    "        cluster_labels = clusterer.fit_predict(X_features)\n",
    "        \n",
    "        sil_avg = silhouette_score(X_features, cluster_labels)\n",
    "        sil_values = silhouette_samples(X_features, cluster_labels)\n",
    "\n",
    "        n_samples, n_features = X_features.shape\n",
    "        n_clusters = len(np.unique(cluster_labels))\n",
    "\n",
    "        y_lower = 12\n",
    "        axs[ind].set_title('Number of Cluster : '+ str(n_cluster)+'\\n' \\\n",
    "                          'Silhouette Score :' + str(round(sil_avg,4)), fontsize=13 )\n",
    "        axs[ind].set_xlabel(\"The silhouette coefficient values\", fontsize=13)\n",
    "        axs[ind].set_ylabel(\"Cluster label\", fontsize=12)\n",
    "        axs[ind].set_xlim([-0.1, 1])\n",
    "        axs[ind].set_ylim([0, n_samples + (n_clusters + 1) * 10])\n",
    "        axs[ind].set_yticks([])  # Clear the yaxis labels / ticks\n",
    "        axs[ind].set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1])\n",
    "        \n",
    "        # 클러스터링 갯수별로 fill_betweenx( )형태의 막대 그래프 표현. \n",
    "        for i in range(n_cluster):\n",
    "            ith_cluster_sil_values = sil_values[cluster_labels==i]\n",
    "            ith_cluster_sil_values.sort()\n",
    "            \n",
    "            size_cluster_i = ith_cluster_sil_values.shape[0]\n",
    "            y_upper = y_lower + size_cluster_i\n",
    "            \n",
    "            color = cm.nipy_spectral(float(i) / n_cluster)\n",
    "            axs[ind].fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_sil_values, \\\n",
    "                                facecolor=color, edgecolor=color, alpha=0.7)\n",
    "            axs[ind].text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
    "            y_lower = y_upper + 10\n",
    "            \n",
    "        axs[ind].axvline(x=sil_avg, color=\"red\", linestyle=\"--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 여러개의 클러스터링 갯수를 List로 입력 받아 각각의 실루엣 계수를 면적으로 시각화한 함수 작성\n",
    "def visualize_silhouette(cluster_lists, X_features): \n",
    "    \n",
    "    from sklearn.datasets import make_blobs\n",
    "    from sklearn.cluster import KMeans\n",
    "    from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "\n",
    "    import matplotlib.pyplot as plt\n",
    "    import matplotlib.cm as cm\n",
    "    import math\n",
    "    \n",
    "    sns.set(style='whitegrid')\n",
    "\n",
    "    \n",
    "    # 입력값으로 클러스터링 갯수들을 리스트로 받아서, 각 갯수별로 클러스터링을 적용하고 실루엣 개수를 구함\n",
    "    n_cols = len(cluster_lists)\n",
    "    \n",
    "    # plt.subplots()으로 리스트에 기재된 클러스터링 수만큼의 sub figures를 가지는 axs 생성 \n",
    "    fig, axs = plt.subplots(figsize=(4*n_cols, 4), nrows=1, ncols=n_cols)\n",
    "    \n",
    "    # 리스트에 기재된 클러스터링 갯수들을 차례로 iteration 수행하면서 실루엣 개수 시각화\n",
    "    for ind, n_cluster in enumerate(cluster_lists):\n",
    "        \n",
    "        # KMeans 클러스터링 수행하고, 실루엣 스코어와 개별 데이터의 실루엣 값 계산. \n",
    "        clusterer = KMeans(n_clusters = n_cluster, max_iter=500, random_state=0)\n",
    "        cluster_labels = clusterer.fit_predict(X_features)\n",
    "        \n",
    "        sil_avg = silhouette_score(X_features, cluster_labels)\n",
    "        sil_values = silhouette_samples(X_features, cluster_labels)\n",
    "\n",
    "        n_samples, n_features = X_features.shape\n",
    "        n_clusters = len(np.unique(cluster_labels))\n",
    "\n",
    "        y_lower = 12\n",
    "        axs[ind].set_title('Number of Cluster : '+ str(n_cluster)+'\\n' \\\n",
    "                          'Silhouette Score :' + str(round(sil_avg,4)), fontsize=16 )\n",
    "        axs[ind].set_xlabel(\"The silhouette coefficient values\", fontsize=13)\n",
    "        axs[ind].set_ylabel(\"Cluster label\", fontsize=12)\n",
    "        axs[ind].set_xlim([-0.005, 0.02])\n",
    "        axs[ind].set_ylim([0, n_samples + (n_clusters + 1) * 10])\n",
    "        axs[ind].set_yticks([])  # Clear the yaxis labels / ticks\n",
    "        axs[ind].set_xticks([0, 0.004, 0.008, 0.012, 0.016, 0.02])\n",
    "        \n",
    "        # 클러스터링 갯수별로 fill_betweenx( )형태의 막대 그래프 표현. \n",
    "        for i in range(n_cluster):\n",
    "            ith_cluster_sil_values = sil_values[cluster_labels==i]\n",
    "            ith_cluster_sil_values.sort()\n",
    "            \n",
    "            size_cluster_i = ith_cluster_sil_values.shape[0]\n",
    "            y_upper = y_lower + size_cluster_i\n",
    "            \n",
    "            color = cm.nipy_spectral(float(i) / n_cluster)\n",
    "            axs[ind].fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_sil_values, \\\n",
    "                                facecolor=color, edgecolor=color, alpha=0.7)\n",
    "            axs[ind].text(-0.0009, y_lower + 0.01 * size_cluster_i, str(i))\n",
    "            y_lower = y_upper + 10\n",
    "            \n",
    "        axs[ind].axvline(x=sil_avg, color=\"red\", linestyle=\"--\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_silhouette([2,3,4,5], features)\n",
    "visualize_silhouette([6,7,8,9], features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(r\".xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(7, random_state=0)\n",
    "kmeans_label= kmeans.fit_predict(features)\n",
    "df['kmeans_label'] = kmeans_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel(r\"C\\df_kmeans.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
